fix CI ?

CI/physmon/workflows/physmon_trackfinding_ttbar_pu200.py

@@ -146,16 +146,6 @@
         outputDirRoot=tp,
     )
 
-    addAmbiguityResolutionML(
-        s,
-        AmbiguityResolutionMLConfig(
-            maximumSharedHits=3, maximumIterations=1000000, nMeasurementsMin=6
-        ),
-        outputDirRoot=tp,
-        onnxModelFile=Path(__file__).resolve().parent.parent.parent.parent
-        / "thirdparty/OpenDataDetector/data/duplicateClassifier.onnx",
-    )
-
     s.addAlgorithm(
         acts.examples.TracksToParameters(
             level=acts.logging.INFO,
@@ -189,6 +179,17 @@
         outputDirRoot=tp / "amvf_grid_time",
     )
 
+    addAmbiguityResolutionML(
+        s,
+        AmbiguityResolutionMLConfig(
+            maximumSharedHits=3, maximumIterations=1000000, nMeasurementsMin=6
+        ),
+        tracks="ckf_tracks",
+        outputDirRoot=tp,
+        onnxModelFile=Path(__file__).resolve().parent.parent.parent.parent
+        / "thirdparty/OpenDataDetector/data/duplicateClassifier.onnx",
+    )
+
     s.run()
 
     shutil.move(

Core/include/Acts/AmbiguityResolution/AmbiguityNetworkConcept.hpp

@@ -25,6 +25,8 @@ using DummyTrackContainer =
 /// The ambiguity network correspond to the AmbiguityTrackClassifier found in
 /// the Onnx plugin. It is used to score the tracks and select the best ones.
 ///
+/// The constructor of the Ambiguity Solver network should take string as input
+/// corresponding to the path of the ONNX model.
 /// The implementation of the Ambiguity Solver network should have two methods:
 /// - inferScores: takes clusters (a list of track ID associated with a cluster
 /// ID) and the track container and return an outputTensor (list of scores for
@@ -37,9 +39,14 @@ template <typename network_t>
 concept AmbiguityNetworkConcept = requires(
     DummyTrackContainer &tracks,
     std::unordered_map<std::size_t, std::vector<std::size_t>> &clusters,
-    std::vector<std::vector<float>> &outputTensor, network_t &n) {
+    std::vector<std::vector<float>> &outputTensor, const char *modelPath,
+    network_t &n) {
   requires TrackContainerFrontend<DummyTrackContainer>;
 
+  { 
+    network_t(modelPath) 
+  } -> std::same_as<network_t>;
+
   {
     n.inferScores(clusters, tracks)
   } -> std::same_as<std::vector<std::vector<float>>>;

Core/include/Acts/AmbiguityResolution/AmbiguityResolutionML.hpp

@@ -29,7 +29,7 @@ template <AmbiguityNetworkConcept AmbiguityNetwork>
 class AmbiguityResolutionML {
  public:
   struct Config {
-    /// Path to the ONNX model for the duplicate neural network
+    /// Path to the model file for the duplicate neural network
     std::string inputDuplicateNN = "";
     /// Minimum number of measurement to form a track.
     std::size_t nMeasurementsMin = 7;

Core/include/Acts/TrackFinding/detail/AmbiguityTrackClustering.hpp

@@ -15,7 +15,7 @@
 
 namespace Acts::detail {
 
-/// Clusterise tracks based on shared hits.
+/// Cluster tracks based on shared hits.
 ///
 /// In this algorithm we will loop through all the tracks by decreasing number
 /// of measurements. Cluster are created when a new track is encountered that